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Parent-Child Relationshipedit
The parent-child relationship is similar in nature to the nested model: both allow you to associate one entity with another. The difference is that, with nested objects, all entities live within the same document while, with parent-child, the parent and children are completely separate documents.
The parent-child functionality allows you to associate one document type with
another, in a one-to-many relationship—one parent to many children. The
advantages that parent-child has over nested
objects are as follows:
- The parent document can be updated without reindexing the children.
- Child documents can be added, changed, or deleted without affecting either the parent or other children. This is especially useful when child documents are large in number and need to be added or changed frequently.
- Child documents can be returned as the results of a search request.
Elasticsearch maintains a map of which parents are associated with which children. It is thanks to this map that query-time joins are fast, but it does place a limitation on the parent-child relationship: the parent document and all of its children must live on the same shard.
The parent-child ID maps are stored in Doc Values, which allows them to execute quickly when fully hot in memory, but scalable enough to spill to disk when the map is very large.
- Elasticsearch - The Definitive Guide:
- Foreword
- Preface
- Getting Started
- You Know, for Search…
- Installing and Running Elasticsearch
- Talking to Elasticsearch
- Document Oriented
- Finding Your Feet
- Indexing Employee Documents
- Retrieving a Document
- Search Lite
- Search with Query DSL
- More-Complicated Searches
- Full-Text Search
- Phrase Search
- Highlighting Our Searches
- Analytics
- Tutorial Conclusion
- Distributed Nature
- Next Steps
- Life Inside a Cluster
- Data In, Data Out
- What Is a Document?
- Document Metadata
- Indexing a Document
- Retrieving a Document
- Checking Whether a Document Exists
- Updating a Whole Document
- Creating a New Document
- Deleting a Document
- Dealing with Conflicts
- Optimistic Concurrency Control
- Partial Updates to Documents
- Retrieving Multiple Documents
- Cheaper in Bulk
- Distributed Document Store
- Searching—The Basic Tools
- Mapping and Analysis
- Full-Body Search
- Sorting and Relevance
- Distributed Search Execution
- Index Management
- Inside a Shard
- You Know, for Search…
- Search in Depth
- Structured Search
- Full-Text Search
- Multifield Search
- Proximity Matching
- Partial Matching
- Controlling Relevance
- Theory Behind Relevance Scoring
- Lucene’s Practical Scoring Function
- Query-Time Boosting
- Manipulating Relevance with Query Structure
- Not Quite Not
- Ignoring TF/IDF
- function_score Query
- Boosting by Popularity
- Boosting Filtered Subsets
- Random Scoring
- The Closer, The Better
- Understanding the price Clause
- Scoring with Scripts
- Pluggable Similarity Algorithms
- Changing Similarities
- Relevance Tuning Is the Last 10%
- Dealing with Human Language
- Aggregations
- Geolocation
- Modeling Your Data
- Administration, Monitoring, and Deployment